Assessment of Oceanic Anomalies of Predictive Potential (D2.5)
Notice bibliographique
Résumé
Climate prediction is the challenge to forecast climatic conditions months to decades into the future with a skill and regional detail that is of practical use. Will, for example, Arctic sea ice cover increase the next winter? Will Scandinavian hydroclimate be particularly beneficial for hydropower production? Will Southern European summers be excessively warm through the 2020s? To what extent such conditions are predictable in nature and to what extent predictability can be realised in operational climate forecast systems and translated to useful stakeholder information, i.e., the climate equivalent to weather forecasting, remain unknown. It is commonly understood that predictability resides with the more inert components of the climate system and particularly—as is the focus of Blue-Action—with ocean circulation. Blue-Action has substantiated this premise by exploring observations, climate models, and reanalyses (model simulations tightly constrained by available observations). Successful avenues of research and progress made in Blue-Action include mapping out the dominant timescales of European interannual-to-decadal climate variability, the identification of consistent and predictable variability in Atlantic-to-Arctic ocean circulation, the link of ocean variability to fluctuating climate over land and sea ice extent, and making actual climate forecasts toward 2020. We find that winter surface air temperatures for much of continental Europe, and generally for Eastern Europe, are characterized by a 5–10 year timescale. This reflects the variable strength of the large-scale westerly winds, commonly summarized in the so-called North Atlantic Oscillation (NAO) index. The longest timescale that our observation-based record resolves is a 25–40 year fluctuation characteristic for Western Europe (Iberia and England). This we associate with more hemisphere-scale coherent temperature fluctuations, the so-called Atlantic Multidecadal Oscillation (AMO). The winter temperature over Northwest Europe (Norway, Sweden and parts of Finland), on the other hand, is dominated by an intermediate 14-year timescale coherent with the sea surface temperature of the Norwegian Sea. Fluctuating warm and cold ocean states off the east coast of Newfoundland progress persistently with ocean circulation to and through the Norwegian Sea subsequently to face the Arctic sea ice cover. Our analyses show that this variable ocean heat arrives predictably in the Norwegian Sea and imprints on the surface air temperature carried in over Scandinavia “downwind” with the mean westerly wind with a forecast horizon of 7–10 years. We particularly predict (from data through to 2016) that Norwegian mean temperature and precipitation will decrease, although very marginally so (and remain above 1981–2010 climatology), toward 2020. And a general mean temperature decrease was indeed observed from 2014 through to 2018 (but only slightly from 2016). Similarly, winter sea ice extent in the Atlantic sector is predicted slightly to increase (but remain below climatology). This is qualitatively reflected in the fact that Arctic March winter maximum increased – also relatively slightly – from 2017 to 2018, and then again from 2018 to 2019. A basis for exploring climate predictability is the access to, and maintenance of, key observational time series. Blue-Action benefits from the collective effort of the scientific community in making repeated observations of the ocean and from satellite remote sensing of the sea surface. Importantly, Blue-Action contributes observations to this effort with full-depth hydrographic and current meter measurements of the water mass exchanges at the Greenland-Scotland Ridge, the main gateway between the North Atlantic and the Arctic region—including the passage into the Norwegian Sea of the Gulf Stream’s northern limb and the cold and warm ocean states giving rise to climate predictability as alluded to above. A priority for Blue-Action is therefore both the maintenance and consistency of these main time series of northern ocean climate, and further to deepen the understanding of the full spectrum of variability reflected at the mooring sites (predictable and otherwise). We find that at seasonal time scales, the vigour of the exchanges generally reflects that of the regional wind (broadly speaking the NAO), but that changes in so-called buoyancy forcing (heat loss and freshwater input) seems increasingly important with increasing timescales. The current meter measurement records, initiated by several of the Blue-Action partners through the 1990s, are still too short for confidently concluding on the nature of observed variability at timescales longer than the interannual. Further progress in “taking the pulse” of the Gulf Stream’s interaction with the Arctic is therefore from an observational perspective critically dependent on the future maintenance and thus increasing temporal range covered by the Greenland-Scotland Ridge observational array.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,007 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,002 | 0,003 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».